Overview

Dataset statistics

Number of variables8
Number of observations75604
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 MiB
Average record size in memory64.0 B

Variable types

Numeric8

Alerts

Calories is highly overall correlated with FatContent and 1 other fieldsHigh correlation
FatContent is highly overall correlated with Calories and 1 other fieldsHigh correlation
ProteinContent is highly overall correlated with Calories and 1 other fieldsHigh correlation
CookTime is highly skewed (γ1 = 62.92065327)Skewed
PrepTime is highly skewed (γ1 = 130.9083158)Skewed
Calories is highly skewed (γ1 = 171.2276992)Skewed
FatContent is highly skewed (γ1 = 211.1441109)Skewed
ProteinContent is highly skewed (γ1 = 211.4521105)Skewed
RecipeId has unique valuesUnique
CookTime has 11781 (15.6%) zerosZeros
PrepTime has 2217 (2.9%) zerosZeros
FatContent has 1609 (2.1%) zerosZeros
FiberContent has 3807 (5.0%) zerosZeros
SugarContent has 1619 (2.1%) zerosZeros
ProteinContent has 1186 (1.6%) zerosZeros

Reproduction

Analysis started2024-01-22 00:22:47.007621
Analysis finished2024-01-22 00:22:53.087095
Duration6.08 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

RecipeId
Real number (ℝ)

UNIQUE 

Distinct75604
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188832.75
Minimum40
Maximum541195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size590.8 KiB
2024-01-22T01:22:53.208369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile16266.75
Q170903.5
median160469
Q3289503.75
95-th percentile449332.3
Maximum541195
Range541155
Interquartile range (IQR)218600.25

Descriptive statistics

Standard deviation136977.01
Coefficient of variation (CV)0.72538798
Kurtosis-0.7213204
Mean188832.75
Median Absolute Deviation (MAD)102934.5
Skewness0.57395087
Sum1.4276512 × 1010
Variance1.8762701 × 1010
MonotonicityNot monotonic
2024-01-22T01:22:53.287575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73440 1
 
< 0.1%
108397 1
 
< 0.1%
22750 1
 
< 0.1%
42333 1
 
< 0.1%
144120 1
 
< 0.1%
37770 1
 
< 0.1%
177175 1
 
< 0.1%
393917 1
 
< 0.1%
56643 1
 
< 0.1%
222398 1
 
< 0.1%
Other values (75594) 75594
> 99.9%
ValueCountFrequency (%)
40 1
< 0.1%
42 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
54 1
< 0.1%
56 1
< 0.1%
58 1
< 0.1%
59 1
< 0.1%
60 1
< 0.1%
62 1
< 0.1%
ValueCountFrequency (%)
541195 1
< 0.1%
540876 1
< 0.1%
540836 1
< 0.1%
540716 1
< 0.1%
540470 1
< 0.1%
540263 1
< 0.1%
540238 1
< 0.1%
539917 1
< 0.1%
539759 1
< 0.1%
539399 1
< 0.1%

CookTime
Real number (ℝ)

SKEWED  ZEROS 

Distinct246
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5490.7111
Minimum0
Maximum10368000
Zeros11781
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size590.8 KiB
2024-01-22T01:22:53.348591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1600
median1200
Q32700
95-th percentile10800
Maximum10368000
Range10368000
Interquartile range (IQR)2100

Descriptive statistics

Standard deviation104346.79
Coefficient of variation (CV)19.004239
Kurtosis4765.3107
Mean5490.7111
Median Absolute Deviation (MAD)900
Skewness62.920653
Sum4.1511972 × 108
Variance1.0888252 × 1010
MonotonicityNot monotonic
2024-01-22T01:22:53.410708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11781
15.6%
1800 7234
 
9.6%
1200 7009
 
9.3%
900 6487
 
8.6%
600 6271
 
8.3%
3600 5018
 
6.6%
1500 3655
 
4.8%
2700 3446
 
4.6%
2400 2824
 
3.7%
300 2769
 
3.7%
Other values (236) 19110
25.3%
ValueCountFrequency (%)
0 11781
15.6%
60 493
 
0.7%
120 480
 
0.6%
180 467
 
0.6%
240 240
 
0.3%
300 2769
 
3.7%
360 368
 
0.5%
420 338
 
0.4%
480 775
 
1.0%
540 132
 
0.2%
ValueCountFrequency (%)
10368000 2
 
< 0.1%
7776000 3
< 0.1%
6480000 2
 
< 0.1%
5184000 5
< 0.1%
4320000 1
 
< 0.1%
3628800 2
 
< 0.1%
3456000 2
 
< 0.1%
3369600 1
 
< 0.1%
2592000 7
< 0.1%
2419200 2
 
< 0.1%

PrepTime
Real number (ℝ)

SKEWED  ZEROS 

Distinct166
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3158.6043
Minimum0
Maximum17280000
Zeros2217
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size590.8 KiB
2024-01-22T01:22:53.468417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile120
Q1600
median900
Q31200
95-th percentile3600
Maximum17280000
Range17280000
Interquartile range (IQR)600

Descriptive statistics

Standard deviation86563.02
Coefficient of variation (CV)27.405465
Kurtosis22701.7
Mean3158.6043
Median Absolute Deviation (MAD)300
Skewness130.90832
Sum2.3880312 × 108
Variance7.4931564 × 109
MonotonicityNot monotonic
2024-01-22T01:22:53.530600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 17452
23.1%
900 15594
20.6%
300 10704
14.2%
1200 10460
13.8%
1800 6730
 
8.9%
0 2217
 
2.9%
1500 2126
 
2.8%
3600 1510
 
2.0%
2700 1301
 
1.7%
120 1097
 
1.5%
Other values (156) 6413
 
8.5%
ValueCountFrequency (%)
0 2217
 
2.9%
60 472
 
0.6%
120 1097
 
1.5%
180 555
 
0.7%
240 142
 
0.2%
300 10704
14.2%
360 93
 
0.1%
420 199
 
0.3%
480 270
 
0.4%
540 8
 
< 0.1%
ValueCountFrequency (%)
17280000 1
 
< 0.1%
7776000 1
 
< 0.1%
5443200 1
 
< 0.1%
5184000 3
< 0.1%
3801600 1
 
< 0.1%
3110400 1
 
< 0.1%
3024000 1
 
< 0.1%
2592000 2
< 0.1%
2160000 1
 
< 0.1%
2073600 3
< 0.1%

Calories
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct14329
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean487.57734
Minimum0
Maximum350473.1
Zeros416
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size590.8 KiB
2024-01-22T01:22:53.589831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56.1
Q1174.7
median318.1
Q3530.4
95-th percentile1304.585
Maximum350473.1
Range350473.1
Interquartile range (IQR)355.7

Descriptive statistics

Standard deviation1496.3829
Coefficient of variation (CV)3.0690165
Kurtosis39617.835
Mean487.57734
Median Absolute Deviation (MAD)165
Skewness171.2277
Sum36862797
Variance2239161.8
MonotonicityNot monotonic
2024-01-22T01:22:53.650487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 416
 
0.6%
176.8 31
 
< 0.1%
113.8 31
 
< 0.1%
181.8 31
 
< 0.1%
164 30
 
< 0.1%
156.2 26
 
< 0.1%
160.8 25
 
< 0.1%
111.8 25
 
< 0.1%
201.7 25
 
< 0.1%
320.1 25
 
< 0.1%
Other values (14319) 74939
99.1%
ValueCountFrequency (%)
0 416
0.6%
0.1 12
 
< 0.1%
0.2 7
 
< 0.1%
0.3 4
 
< 0.1%
0.4 3
 
< 0.1%
0.5 3
 
< 0.1%
0.6 9
 
< 0.1%
0.7 4
 
< 0.1%
0.8 4
 
< 0.1%
0.9 5
 
< 0.1%
ValueCountFrequency (%)
350473.1 1
< 0.1%
45609 1
< 0.1%
43924.6 1
< 0.1%
38680.1 1
< 0.1%
35503.9 1
< 0.1%
23001.1 1
< 0.1%
22298.4 1
< 0.1%
21190.1 1
< 0.1%
19382.1 1
< 0.1%
18268.7 1
< 0.1%

FatContent
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2446
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.781641
Minimum0
Maximum30123.7
Zeros1609
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size590.8 KiB
2024-01-22T01:22:53.709027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q15.6
median13.7
Q327.5
95-th percentile74.6
Maximum30123.7
Range30123.7
Interquartile range (IQR)21.9

Descriptive statistics

Standard deviation119.77561
Coefficient of variation (CV)4.8332395
Kurtosis52772.645
Mean24.781641
Median Absolute Deviation (MAD)9.7
Skewness211.14411
Sum1873591.2
Variance14346.196
MonotonicityNot monotonic
2024-01-22T01:22:53.768671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1609
 
2.1%
0.1 1319
 
1.7%
0.2 937
 
1.2%
0.3 685
 
0.9%
0.5 465
 
0.6%
0.4 459
 
0.6%
0.6 424
 
0.6%
0.8 368
 
0.5%
0.7 347
 
0.5%
7 328
 
0.4%
Other values (2436) 68663
90.8%
ValueCountFrequency (%)
0 1609
2.1%
0.1 1319
1.7%
0.2 937
1.2%
0.3 685
0.9%
0.4 459
 
0.6%
0.5 465
 
0.6%
0.6 424
 
0.6%
0.7 347
 
0.5%
0.8 368
 
0.5%
0.9 302
 
0.4%
ValueCountFrequency (%)
30123.7 1
< 0.1%
4012.1 1
< 0.1%
2806.6 1
< 0.1%
2196.7 1
< 0.1%
1661.9 1
< 0.1%
1653.2 1
< 0.1%
1612.3 1
< 0.1%
1466.2 1
< 0.1%
1399.5 1
< 0.1%
1289.8 1
< 0.1%

FiberContent
Real number (ℝ)

ZEROS 

Distinct618
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8317787
Minimum0
Maximum519.5
Zeros3807
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size590.8 KiB
2024-01-22T01:22:53.827090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8
median2.1
Q34.5
95-th percentile12.7
Maximum519.5
Range519.5
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation6.8206457
Coefficient of variation (CV)1.7800208
Kurtosis919.8529
Mean3.8317787
Median Absolute Deviation (MAD)1.6
Skewness18.361194
Sum289697.8
Variance46.521208
MonotonicityNot monotonic
2024-01-22T01:22:53.887998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3807
 
5.0%
0.1 2345
 
3.1%
0.2 2090
 
2.8%
0.3 1998
 
2.6%
0.6 1916
 
2.5%
0.8 1910
 
2.5%
0.5 1841
 
2.4%
1.1 1751
 
2.3%
0.7 1748
 
2.3%
0.4 1742
 
2.3%
Other values (608) 54456
72.0%
ValueCountFrequency (%)
0 3807
5.0%
0.1 2345
3.1%
0.2 2090
2.8%
0.3 1998
2.6%
0.4 1742
2.3%
0.5 1841
2.4%
0.6 1916
2.5%
0.7 1748
2.3%
0.8 1910
2.5%
0.9 1654
2.2%
ValueCountFrequency (%)
519.5 1
< 0.1%
472 1
< 0.1%
299.6 1
< 0.1%
265.6 1
< 0.1%
258.5 1
< 0.1%
220.2 1
< 0.1%
195.2 1
< 0.1%
192.4 1
< 0.1%
190.3 1
< 0.1%
179.6 1
< 0.1%

SugarContent
Real number (ℝ)

ZEROS 

Distinct2849
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.073483
Minimum0
Maximum4735.8
Zeros1619
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size590.8 KiB
2024-01-22T01:22:53.946876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q12.5
median6.4
Q318.1
95-th percentile71.7
Maximum4735.8
Range4735.8
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation71.59193
Coefficient of variation (CV)3.2433454
Kurtosis786.80925
Mean22.073483
Median Absolute Deviation (MAD)5.1
Skewness18.788056
Sum1668843.6
Variance5125.4044
MonotonicityNot monotonic
2024-01-22T01:22:54.005860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1619
 
2.1%
0.1 1222
 
1.6%
0.2 919
 
1.2%
0.3 824
 
1.1%
1.6 794
 
1.1%
0.8 778
 
1.0%
0.4 751
 
1.0%
1.1 750
 
1.0%
1.4 731
 
1.0%
1.8 723
 
1.0%
Other values (2839) 66493
87.9%
ValueCountFrequency (%)
0 1619
2.1%
0.1 1222
1.6%
0.2 919
1.2%
0.3 824
1.1%
0.4 751
1.0%
0.5 700
0.9%
0.6 699
0.9%
0.7 678
0.9%
0.8 778
1.0%
0.9 665
0.9%
ValueCountFrequency (%)
4735.8 1
< 0.1%
4531.8 1
< 0.1%
4225.3 1
< 0.1%
2894.1 1
< 0.1%
2486.8 1
< 0.1%
2387.5 1
< 0.1%
2195.4 1
< 0.1%
2171 1
< 0.1%
2148.6 1
< 0.1%
1883.5 1
< 0.1%

ProteinContent
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1458
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.647143
Minimum0
Maximum18396.2
Zeros1186
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size590.8 KiB
2024-01-22T01:22:54.062575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q13.5
median9.1
Q325.1
95-th percentile53.7
Maximum18396.2
Range18396.2
Interquartile range (IQR)21.6

Descriptive statistics

Standard deviation73.211036
Coefficient of variation (CV)4.1486056
Kurtosis52601.529
Mean17.647143
Median Absolute Deviation (MAD)7.3
Skewness211.45211
Sum1334194.6
Variance5359.8558
MonotonicityNot monotonic
2024-01-22T01:22:54.123929image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1186
 
1.6%
0.1 697
 
0.9%
1.1 619
 
0.8%
1.4 598
 
0.8%
0.2 581
 
0.8%
2.5 560
 
0.7%
3 559
 
0.7%
0.8 547
 
0.7%
1.6 543
 
0.7%
2.1 541
 
0.7%
Other values (1448) 69173
91.5%
ValueCountFrequency (%)
0 1186
1.6%
0.1 697
0.9%
0.2 581
0.8%
0.3 505
0.7%
0.4 435
 
0.6%
0.5 451
 
0.6%
0.6 486
0.6%
0.7 507
0.7%
0.8 547
0.7%
0.9 532
0.7%
ValueCountFrequency (%)
18396.2 1
< 0.1%
3270.3 1
< 0.1%
2178.2 1
< 0.1%
1634 1
< 0.1%
1227.1 1
< 0.1%
1188.4 1
< 0.1%
1140.8 1
< 0.1%
1123.4 1
< 0.1%
1113.5 1
< 0.1%
1112.5 1
< 0.1%

Interactions

2024-01-22T01:22:52.505338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:48.220697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.299982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.021377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.578817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.009746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.450716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.872818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.553992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:48.319945image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.367247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.147010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.628294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.061808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.500307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.932373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.607290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:48.416166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.423171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.219905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.684530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.113766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.550009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.984438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.658337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:48.504719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.477866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.279254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.737932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.172334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.601685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.034769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.708160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.014678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.736488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.337240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.786665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.238805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.657690image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.081334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.756579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.114825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.806439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.399469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.838182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.289420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.702612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.126826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.805416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.182884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.873425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.479228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.885289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.338861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.754956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.172097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.851020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.244475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:49.951108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.528103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:50.952709image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.399455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:51.801094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-01-22T01:22:52.459339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-01-22T01:22:54.166120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
CaloriesCookTimeFatContentFiberContentPrepTimeProteinContentRecipeIdSugarContent
Calories1.0000.2300.8290.4580.1650.720-0.0020.353
CookTime0.2301.0000.1910.1570.2330.264-0.0040.101
FatContent0.8290.1911.0000.2740.1690.635-0.0040.148
FiberContent0.4580.1570.2741.0000.1260.396-0.0020.297
PrepTime0.1650.2330.1690.1261.0000.150-0.0010.099
ProteinContent0.7200.2640.6350.3960.1501.000-0.006-0.020
RecipeId-0.002-0.004-0.004-0.002-0.001-0.0061.0000.005
SugarContent0.3530.1010.1480.2970.099-0.0200.0051.000

Missing values

2024-01-22T01:22:52.912435image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-22T01:22:52.995046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RecipeIdCookTimePrepTimeCaloriesFatContentFiberContentSugarContentProteinContent
07344001800241.310.12.31.46.7
13657183600600370.817.51.62.29.4
214175736002700377.620.93.86.112.9
3280351180001800282.816.52.32.711.7
418050536001800257.58.60.430.26.3
5350271120030076.13.61.68.11.2
62151827001200343.818.01.731.45.0
713714312001500526.027.26.110.849.2
82115634200600326.019.20.934.33.1
929280030069.40.00.00.00.0
RecipeIdCookTimePrepTimeCaloriesFatContentFiberContentSugarContentProteinContent
755942750023000600242.07.13.62.28.4
755957495827001800391.023.42.41.913.5
7559633381518001200535.526.15.49.330.1
755972672532700720325.421.51.11.513.4
755981700121200600624.719.26.03.922.3
755992535774320028800121.50.57.80.67.9
7560026782736002700652.225.87.57.250.1
756012669831800900223.99.21.11.726.7
756022537393001202229.880.315.7317.926.7
7560378171960600654.113.83.94.221.8